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Computational aspects of many-body potentials

  • Three decades of many-body potentials in materials research
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Abstract

We discuss the relative complexity and computational cost of several popular many-body empirical potentials, developed by the materials science community over the past 30 years. The inclusion of more detailed many-body effects has come at a computational cost, but the cost still scales linearly with the number of atoms modeled. This is enabling very large molecular dynamics simulations with unprecedented atomic-scale fidelity to physical and chemical phenomena. The cost and scalability of the potentials, run in serial and parallel, are benchmarked in the LAMMPS molecular dynamics code. Several recent large calculations performed with these potentials are highlighted to illustrate what is now possible on current supercomputers. We conclude with a brief mention of high-performance computing architecture trends and the research issues they raise for continued potential development and use.

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Notes

  1. * The comparison can also be stretched too far in the other direction. The analog for computer design of new materials would be to use CGI to create an improved version of George Clooney or Meryl Streep, something not even Pixar is likely planning.

  2. † The benchmarks timings presented are for second-generation REBO21 and second-generation COMB22 potentials, which are later versions than the dates listed in Table I. Recent enhancements to the COMB implementation in LAMMPS show speed-ups of about 2x relative to what is presented here, but our Cray XT5 machine was not available to re-run the benchmarks.

  3. ‡ Since the LJ potential was published in 1924, we did not include it in the plot; it is an outlier in our otherwise compelling Moore’s law analysis!

  4. § For comparison, high-strength steel has a tensile strength of ~2 GPa; Kevlar is ~3.5 GPa.

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Acknowledgments

We thank the following collaborators for their implementation of many-body potentials in LAMMPS that we discussed: Tzu-Ray Shan (COMB, U Florida/SNL), Metin Aktulga (ReaxFF, LBNL), Greg Wagner (MEAM, SNL), Don Ward (BOP, SNL), and Ase Henry (AIREBO and REBO, Georgia Tech). We also thank Alison Kubota (SNL), Charles Cornwell (US Army ERDC), Priya Vashishta (USC), and Matt Lane (SNL) for providing figures to acompany discussion of their work.

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Authors and Affiliations

  1. Sandia National Laboratories, Albuquerque, NM, USA

    Steven J. Plimpton

  2. Scalable Algorithms Department, Sandia National Laboratories, Albuquerque, NM, USA

    Aidan P. Thompson

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Correspondence to Steven J. Plimpton.

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Plimpton, S.J., Thompson, A.P. Computational aspects of many-body potentials. MRS Bulletin 37, 513–521 (2012). https://doi.org/10.1557/mrs.2012.96

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